House Price Prediction Dataset¶

House price prediction refers to the process of estimating the future or current values of a residential property based on various factors and historical data.The goal is to forecast the price of a house given its characteristics and the context of the real estate market.

let's install and import the libraries. We'll use the matplotlib.pyplot module for basic plots like line & bar charts. It is often imported with the alias plt. We'll use the seaborn module for more advanced plots. It is commonly imported with the alias sns.

In [1]:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore') # if there are any warning due to version mismatch, it will be ignored

Load dataset¶

In [2]:
df=pd.read_csv('data (1).csv')
df.head()
Out[2]:
date price bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition sqft_above sqft_basement yr_built yr_renovated street city statezip country
0 2014-05-02 00:00:00 313000.0 3.0 1.50 1340 7912 1.5 0 0 3 1340 0 1955 2005 18810 Densmore Ave N Shoreline WA 98133 USA
1 2014-05-02 00:00:00 2384000.0 5.0 2.50 3650 9050 2.0 0 4 5 3370 280 1921 0 709 W Blaine St Seattle WA 98119 USA
2 2014-05-02 00:00:00 342000.0 3.0 2.00 1930 11947 1.0 0 0 4 1930 0 1966 0 26206-26214 143rd Ave SE Kent WA 98042 USA
3 2014-05-02 00:00:00 420000.0 3.0 2.25 2000 8030 1.0 0 0 4 1000 1000 1963 0 857 170th Pl NE Bellevue WA 98008 USA
4 2014-05-02 00:00:00 550000.0 4.0 2.50 1940 10500 1.0 0 0 4 1140 800 1976 1992 9105 170th Ave NE Redmond WA 98052 USA

Basic Visualization¶

In [3]:
df.shape # checking the n0.of rows and columns
Out[3]:
(4600, 18)
In [4]:
df.head() #Display the first 5rows in the dataframe
Out[4]:
date price bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition sqft_above sqft_basement yr_built yr_renovated street city statezip country
0 2014-05-02 00:00:00 313000.0 3.0 1.50 1340 7912 1.5 0 0 3 1340 0 1955 2005 18810 Densmore Ave N Shoreline WA 98133 USA
1 2014-05-02 00:00:00 2384000.0 5.0 2.50 3650 9050 2.0 0 4 5 3370 280 1921 0 709 W Blaine St Seattle WA 98119 USA
2 2014-05-02 00:00:00 342000.0 3.0 2.00 1930 11947 1.0 0 0 4 1930 0 1966 0 26206-26214 143rd Ave SE Kent WA 98042 USA
3 2014-05-02 00:00:00 420000.0 3.0 2.25 2000 8030 1.0 0 0 4 1000 1000 1963 0 857 170th Pl NE Bellevue WA 98008 USA
4 2014-05-02 00:00:00 550000.0 4.0 2.50 1940 10500 1.0 0 0 4 1140 800 1976 1992 9105 170th Ave NE Redmond WA 98052 USA
In [5]:
df = df.drop_duplicates()
df.head()
Out[5]:
date price bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition sqft_above sqft_basement yr_built yr_renovated street city statezip country
0 2014-05-02 00:00:00 313000.0 3.0 1.50 1340 7912 1.5 0 0 3 1340 0 1955 2005 18810 Densmore Ave N Shoreline WA 98133 USA
1 2014-05-02 00:00:00 2384000.0 5.0 2.50 3650 9050 2.0 0 4 5 3370 280 1921 0 709 W Blaine St Seattle WA 98119 USA
2 2014-05-02 00:00:00 342000.0 3.0 2.00 1930 11947 1.0 0 0 4 1930 0 1966 0 26206-26214 143rd Ave SE Kent WA 98042 USA
3 2014-05-02 00:00:00 420000.0 3.0 2.25 2000 8030 1.0 0 0 4 1000 1000 1963 0 857 170th Pl NE Bellevue WA 98008 USA
4 2014-05-02 00:00:00 550000.0 4.0 2.50 1940 10500 1.0 0 0 4 1140 800 1976 1992 9105 170th Ave NE Redmond WA 98052 USA

Renaming Columns¶

In [6]:
df.rename(columns={'floors': 'levels'}).head(5)
Out[6]:
date price bedrooms bathrooms sqft_living sqft_lot levels waterfront view condition sqft_above sqft_basement yr_built yr_renovated street city statezip country
0 2014-05-02 00:00:00 313000.0 3.0 1.50 1340 7912 1.5 0 0 3 1340 0 1955 2005 18810 Densmore Ave N Shoreline WA 98133 USA
1 2014-05-02 00:00:00 2384000.0 5.0 2.50 3650 9050 2.0 0 4 5 3370 280 1921 0 709 W Blaine St Seattle WA 98119 USA
2 2014-05-02 00:00:00 342000.0 3.0 2.00 1930 11947 1.0 0 0 4 1930 0 1966 0 26206-26214 143rd Ave SE Kent WA 98042 USA
3 2014-05-02 00:00:00 420000.0 3.0 2.25 2000 8030 1.0 0 0 4 1000 1000 1963 0 857 170th Pl NE Bellevue WA 98008 USA
4 2014-05-02 00:00:00 550000.0 4.0 2.50 1940 10500 1.0 0 0 4 1140 800 1976 1992 9105 170th Ave NE Redmond WA 98052 USA
In [7]:
df.rename(columns={'price': 'cost'}).head(10)
Out[7]:
date cost bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition sqft_above sqft_basement yr_built yr_renovated street city statezip country
0 2014-05-02 00:00:00 313000.0 3.0 1.50 1340 7912 1.5 0 0 3 1340 0 1955 2005 18810 Densmore Ave N Shoreline WA 98133 USA
1 2014-05-02 00:00:00 2384000.0 5.0 2.50 3650 9050 2.0 0 4 5 3370 280 1921 0 709 W Blaine St Seattle WA 98119 USA
2 2014-05-02 00:00:00 342000.0 3.0 2.00 1930 11947 1.0 0 0 4 1930 0 1966 0 26206-26214 143rd Ave SE Kent WA 98042 USA
3 2014-05-02 00:00:00 420000.0 3.0 2.25 2000 8030 1.0 0 0 4 1000 1000 1963 0 857 170th Pl NE Bellevue WA 98008 USA
4 2014-05-02 00:00:00 550000.0 4.0 2.50 1940 10500 1.0 0 0 4 1140 800 1976 1992 9105 170th Ave NE Redmond WA 98052 USA
5 2014-05-02 00:00:00 490000.0 2.0 1.00 880 6380 1.0 0 0 3 880 0 1938 1994 522 NE 88th St Seattle WA 98115 USA
6 2014-05-02 00:00:00 335000.0 2.0 2.00 1350 2560 1.0 0 0 3 1350 0 1976 0 2616 174th Ave NE Redmond WA 98052 USA
7 2014-05-02 00:00:00 482000.0 4.0 2.50 2710 35868 2.0 0 0 3 2710 0 1989 0 23762 SE 253rd Pl Maple Valley WA 98038 USA
8 2014-05-02 00:00:00 452500.0 3.0 2.50 2430 88426 1.0 0 0 4 1570 860 1985 0 46611-46625 SE 129th St North Bend WA 98045 USA
9 2014-05-02 00:00:00 640000.0 4.0 2.00 1520 6200 1.5 0 0 3 1520 0 1945 2010 6811 55th Ave NE Seattle WA 98115 USA
In [8]:
df.tail() #provides last 5 samples
Out[8]:
date price bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition sqft_above sqft_basement yr_built yr_renovated street city statezip country
4595 2014-07-09 00:00:00 308166.666667 3.0 1.75 1510 6360 1.0 0 0 4 1510 0 1954 1979 501 N 143rd St Seattle WA 98133 USA
4596 2014-07-09 00:00:00 534333.333333 3.0 2.50 1460 7573 2.0 0 0 3 1460 0 1983 2009 14855 SE 10th Pl Bellevue WA 98007 USA
4597 2014-07-09 00:00:00 416904.166667 3.0 2.50 3010 7014 2.0 0 0 3 3010 0 2009 0 759 Ilwaco Pl NE Renton WA 98059 USA
4598 2014-07-10 00:00:00 203400.000000 4.0 2.00 2090 6630 1.0 0 0 3 1070 1020 1974 0 5148 S Creston St Seattle WA 98178 USA
4599 2014-07-10 00:00:00 220600.000000 3.0 2.50 1490 8102 2.0 0 0 4 1490 0 1990 0 18717 SE 258th St Covington WA 98042 USA
In [9]:
df.columns
Out[9]:
Index(['date', 'price', 'bedrooms', 'bathrooms', 'sqft_living', 'sqft_lot',
       'floors', 'waterfront', 'view', 'condition', 'sqft_above',
       'sqft_basement', 'yr_built', 'yr_renovated', 'street', 'city',
       'statezip', 'country'],
      dtype='object')
In [10]:
df.sample()
Out[10]:
date price bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition sqft_above sqft_basement yr_built yr_renovated street city statezip country
2044 2014-06-06 00:00:00 185000.0 3.0 1.0 1840 8100 1.0 0 0 4 920 920 1953 1983 16433 12th Ave SW Burien WA 98166 USA
In [11]:
df.sample(10)
Out[11]:
date price bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition sqft_above sqft_basement yr_built yr_renovated street city statezip country
384 2014-05-08 00:00:00 3.219500e+05 2.0 1.25 860 1277 2.0 0 0 3 860 0 2007 0 2113 15th Ave S Seattle WA 98144 USA
2224 2014-06-10 00:00:00 4.030000e+05 2.0 1.00 1100 3598 1.0 0 0 4 1100 0 1926 1993 3242 15th Ave S Seattle WA 98144 USA
4381 2014-05-12 00:00:00 5.719861e+05 3.0 2.50 3720 11610 2.0 0 0 3 3720 0 1982 0 21730 NE 29th St Sammamish WA 98074 USA
2937 2014-06-20 00:00:00 6.659000e+05 4.0 2.25 2870 5453 2.0 0 1 4 2220 650 1926 1993 10030 44th Ave SW Seattle WA 98146 USA
4588 2014-07-08 00:00:00 0.000000e+00 4.0 2.25 2890 18226 3.0 1 4 3 2890 0 1984 0 3227-3399 Mountain View Ave N Renton WA 98056 USA
1827 2014-06-03 00:00:00 5.750000e+05 4.0 2.75 3120 7644 2.0 0 0 3 3120 0 2010 0 9423 Ash Ave SE Snoqualmie WA 98065 USA
3492 2014-06-26 00:00:00 2.195000e+05 3.0 1.00 1090 6710 1.5 0 0 5 1090 0 1912 0 116 J St SE Auburn WA 98002 USA
1448 2014-05-28 00:00:00 5.550000e+05 3.0 2.50 3160 4270 2.0 0 0 3 2650 510 2006 0 11131 NE 162nd St Bothell WA 98011 USA
836 2014-05-16 00:00:00 3.300000e+05 3.0 1.50 1170 4950 1.0 0 0 4 1170 0 1960 2001 15421 SE 4th Pl Bellevue WA 98007 USA
1617 2014-05-30 00:00:00 1.365000e+06 3.0 2.50 2090 6000 1.5 0 0 4 2090 0 1928 0 3832 43rd Ave NE Seattle WA 98105 USA
In [12]:
df.isnull().sum() # check for missing values
Out[12]:
date             0
price            0
bedrooms         0
bathrooms        0
sqft_living      0
sqft_lot         0
floors           0
waterfront       0
view             0
condition        0
sqft_above       0
sqft_basement    0
yr_built         0
yr_renovated     0
street           0
city             0
statezip         0
country          0
dtype: int64

DATA CLEANING¶

Handle missing values,outliers,and inconsistencies in the data.This may involve imputation or removal of problematic data points.

In [13]:
df.info() # method prints information about the DataFrame.

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4600 entries, 0 to 4599
Data columns (total 18 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   date           4600 non-null   object 
 1   price          4600 non-null   float64
 2   bedrooms       4600 non-null   float64
 3   bathrooms      4600 non-null   float64
 4   sqft_living    4600 non-null   int64  
 5   sqft_lot       4600 non-null   int64  
 6   floors         4600 non-null   float64
 7   waterfront     4600 non-null   int64  
 8   view           4600 non-null   int64  
 9   condition      4600 non-null   int64  
 10  sqft_above     4600 non-null   int64  
 11  sqft_basement  4600 non-null   int64  
 12  yr_built       4600 non-null   int64  
 13  yr_renovated   4600 non-null   int64  
 14  street         4600 non-null   object 
 15  city           4600 non-null   object 
 16  statezip       4600 non-null   object 
 17  country        4600 non-null   object 
dtypes: float64(4), int64(9), object(5)
memory usage: 647.0+ KB
In [14]:
df.describe() #calculates summary statistics for all numerical columns in the Dataframe.
Out[14]:
price bedrooms bathrooms sqft_living sqft_lot floors waterfront view condition sqft_above sqft_basement yr_built yr_renovated
count 4.600000e+03 4600.000000 4600.000000 4600.000000 4.600000e+03 4600.000000 4600.000000 4600.000000 4600.000000 4600.000000 4600.000000 4600.000000 4600.000000
mean 5.519630e+05 3.400870 2.160815 2139.346957 1.485252e+04 1.512065 0.007174 0.240652 3.451739 1827.265435 312.081522 1970.786304 808.608261
std 5.638347e+05 0.908848 0.783781 963.206916 3.588444e+04 0.538288 0.084404 0.778405 0.677230 862.168977 464.137228 29.731848 979.414536
min 0.000000e+00 0.000000 0.000000 370.000000 6.380000e+02 1.000000 0.000000 0.000000 1.000000 370.000000 0.000000 1900.000000 0.000000
25% 3.228750e+05 3.000000 1.750000 1460.000000 5.000750e+03 1.000000 0.000000 0.000000 3.000000 1190.000000 0.000000 1951.000000 0.000000
50% 4.609435e+05 3.000000 2.250000 1980.000000 7.683000e+03 1.500000 0.000000 0.000000 3.000000 1590.000000 0.000000 1976.000000 0.000000
75% 6.549625e+05 4.000000 2.500000 2620.000000 1.100125e+04 2.000000 0.000000 0.000000 4.000000 2300.000000 610.000000 1997.000000 1999.000000
max 2.659000e+07 9.000000 8.000000 13540.000000 1.074218e+06 3.500000 1.000000 4.000000 5.000000 9410.000000 4820.000000 2014.000000 2014.000000
In [15]:
df.describe().transpose()
Out[15]:
count mean std min 25% 50% 75% max
price 4600.0 551962.988473 563834.702547 0.0 322875.00 460943.461539 654962.50 26590000.0
bedrooms 4600.0 3.400870 0.908848 0.0 3.00 3.000000 4.00 9.0
bathrooms 4600.0 2.160815 0.783781 0.0 1.75 2.250000 2.50 8.0
sqft_living 4600.0 2139.346957 963.206916 370.0 1460.00 1980.000000 2620.00 13540.0
sqft_lot 4600.0 14852.516087 35884.436145 638.0 5000.75 7683.000000 11001.25 1074218.0
floors 4600.0 1.512065 0.538288 1.0 1.00 1.500000 2.00 3.5
waterfront 4600.0 0.007174 0.084404 0.0 0.00 0.000000 0.00 1.0
view 4600.0 0.240652 0.778405 0.0 0.00 0.000000 0.00 4.0
condition 4600.0 3.451739 0.677230 1.0 3.00 3.000000 4.00 5.0
sqft_above 4600.0 1827.265435 862.168977 370.0 1190.00 1590.000000 2300.00 9410.0
sqft_basement 4600.0 312.081522 464.137228 0.0 0.00 0.000000 610.00 4820.0
yr_built 4600.0 1970.786304 29.731848 1900.0 1951.00 1976.000000 1997.00 2014.0
yr_renovated 4600.0 808.608261 979.414536 0.0 0.00 0.000000 1999.00 2014.0
In [16]:
df["date"]=pd.to_datetime(df["date"])
In [17]:
df.dtypes
Out[17]:
date             datetime64[ns]
price                   float64
bedrooms                float64
bathrooms               float64
sqft_living               int64
sqft_lot                  int64
floors                  float64
waterfront                int64
view                      int64
condition                 int64
sqft_above                int64
sqft_basement             int64
yr_built                  int64
yr_renovated              int64
street                   object
city                     object
statezip                 object
country                  object
dtype: object
In [18]:
df.duplicated().sum()
Out[18]:
np.int64(0)
In [19]:
df.groupby("sqft_lot")[["bedrooms","bathrooms","floors","view","condition"]].sum().sort_values(by="sqft_lot",ascending=False).head(15)
Out[19]:
bedrooms bathrooms floors view condition
sqft_lot
1074218 5.0 3.25 1.5 0 5
641203 2.0 2.00 2.0 0 3
478288 3.0 1.75 1.5 3 4
435600 6.0 5.50 3.5 3 5
423838 2.0 1.00 1.0 2 5
389126 3.0 1.00 1.5 0 4
327135 3.0 2.50 2.0 0 3
307752 7.0 8.00 3.0 4 3
306848 3.0 1.00 1.0 0 3
284011 4.0 4.50 2.0 0 4
280962 2.0 1.75 2.0 2 3
265716 4.0 1.75 1.0 0 4
258746 5.0 3.00 1.5 0 4
251341 3.0 2.00 2.0 0 3
250470 3.0 1.75 1.0 0 4

DataFrame showing the sum of bedrooms, bathrooms, floors, view, and condition for each unique sqft_lot, sorted in descending order by sqft_lot, and displaying the top 15 rows.

Data Visualisation¶

In [20]:
num_col=df[df.dtypes[df.dtypes != 'object'].index]
num_col

plt.figure(figsize=(15,10))
sns.heatmap(num_col.corr(),annot=True)
Out[20]:
<Axes: >
No description has been provided for this image

The code generates a heatmap showing the correlation matrix of the numeric columns in the DataFrame df, with correlation coefficients annotated on the heatmap.

In [21]:
sns.set(rc={'figure.figsize':(12,8)})
sns.distplot(df['bathrooms'],kde=False,bins=20);   ##Kernal density estimation
No description has been provided for this image

his will generate and display a histogram of the bathrooms column with 20 bins, and it will not include a kernel density estimate (KDE) curve. The figure size is set to 12x8 inches, as specified.

In [22]:
sns.kdeplot(df['bathrooms']) # Kernal Density Estimation
Out[22]:
<Axes: xlabel='bathrooms', ylabel='Density'>
No description has been provided for this image

A KDE plot of the bathrooms column, illustrating the estimated probability density function of the data distribution.

In [23]:
df['bathrooms'].describe()
Out[23]:
count    4600.000000
mean        2.160815
std         0.783781
min         0.000000
25%         1.750000
50%         2.250000
75%         2.500000
max         8.000000
Name: bathrooms, dtype: float64

Summary statistics including count, mean, standard deviation, minimum, 25th percentile, median (50th percentile), 75th percentile, and maximum values of the bathrooms column.

In [24]:
sns.set(rc={'figure.figsize':(12,8)})
sns.distplot(df['bedrooms'],bins=20);
No description has been provided for this image

A histogram of the bedrooms column with 20 bins, displaying the distribution of the data.

Histogram¶¶

In exploratory data analysis(EDA),a histogram is a graphical representation that shows the distibution of a dataset.It display the frequency of data points falling within specified ranges or bins.By plotting a histogram,you can quickly see patterns,such as the distribution shape,cental tendency,and spread of the data.It helps in identifying skewness, outliers,and the overall distribution of the data,which is essential for understanding the underlying patterns and macking informed decisions.

In [25]:
plt.figure(figsize=(7,3))

sns.histplot(x="bedrooms",data=df,color="red",binwidth=1)
Out[25]:
<Axes: xlabel='bedrooms', ylabel='Count'>
No description has been provided for this image

A red histogram of the bedrooms column with bin width of 1, displayed in a 7x3 inch figure.

In [26]:
plt.figure(figsize=(10,5))

sns.countplot(x='bedrooms',data=df)
Out[26]:
<Axes: xlabel='bedrooms', ylabel='count'>
No description has been provided for this image

A bar plot showing the count of each unique value in the bedrooms column, displayed in a 10x5 inch figure.

In [27]:
plt.figure(figsize=(10,5))

sns.countplot(x='bedrooms',data=df)
plt.xticks(rotation=90)
Out[27]:
([0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
 [Text(0, 0, '0.0'),
  Text(1, 0, '1.0'),
  Text(2, 0, '2.0'),
  Text(3, 0, '3.0'),
  Text(4, 0, '4.0'),
  Text(5, 0, '5.0'),
  Text(6, 0, '6.0'),
  Text(7, 0, '7.0'),
  Text(8, 0, '8.0'),
  Text(9, 0, '9.0')])
No description has been provided for this image

This code generates a bar plot of counts for each unique value in the bedrooms column from the DataFrame df, with the figure size set to 10x5 inches and x-axis labels rotated 90 degrees.

Scatterplot¶

A scatter plot is a two-dimentional data visualization that uses dots to represent the values obtained for two different variables-one is plotted along the x-axis and the other plotted along the y-axis.

In [28]:
plt.scatter(x="bedrooms",y="bathrooms",data=df)
plt.xlabel("Bedrooms")
plt.ylabel("Bathrooms")
plt.title("Scatter plot of Bedrooms vs Bathrooms")
plt.show()
No description has been provided for this image

A scatter plot of bedrooms vs bathrooms with labeled axes and a title, showing the relationship between the two variables.

In [29]:
sns.scatterplot(x="bedrooms",y="bathrooms",data=df)
Out[29]:
<Axes: xlabel='bedrooms', ylabel='bathrooms'>
No description has been provided for this image

A scatter plot showing the relationship between bedrooms and bathrooms.

In [30]:
sns.regplot(x="bedrooms",y="bathrooms",data=df,scatter=True,fit_reg=True)
Out[30]:
<Axes: xlabel='bedrooms', ylabel='bathrooms'>
No description has been provided for this image

This code generates a scatter plot of bedrooms versus bathrooms with a fitted regression line, showing both the data points and the regression line.

In [31]:
sns.regplot(x="bedrooms",y="bathrooms",data=df,scatter=True,fit_reg=False)
Out[31]:
<Axes: xlabel='bedrooms', ylabel='bathrooms'>
No description has been provided for this image
In [32]:
import pandas as pd
col_names = ['sepal_length','sepal_width','petal_length','petal_width','species']

csv_url = 'data (1).csv'
flowers_df = pd.read_csv(csv_url, names  = col_names)
In [33]:
flowers_df.columns
Out[33]:
Index(['sepal_length', 'sepal_width', 'petal_length', 'petal_width',
       'species'],
      dtype='object')
In [34]:
flowers_df['species'].unique()
Out[34]:
array(['country', 'USA'], dtype=object)
In [35]:
plt.plot(flowers_df.sepal_length,flowers_df.sepal_width)
plt.show()
No description has been provided for this image

A line plot of sepal_length vs sepal_width showing the trend between these two variables.

In [36]:
plt.plot(flowers_df.petal_length,flowers_df.petal_width);
plt.show()
No description has been provided for this image

A line plot of petal_length vs petal_width, showing the trend between these two variables.

In [37]:
sns.scatterplot(x=flowers_df.sepal_length,y=flowers_df.sepal_width);
plt.show()
No description has been provided for this image

This code generates a scatter plot of sepal_length versus sepal_width from the flowers_df DataFrame, displaying the individual data points for these two variables.

In [38]:
sns.scatterplot(x=flowers_df.petal_length,y=flowers_df.petal_width);
plt.show()
No description has been provided for this image

A scatter plot of petal_length versus petal_width, showing the distribution of data points for these variables.

In [39]:
sns.pairplot(df)
plt.show()
No description has been provided for this image

A matrix of scatter plots and histograms showing pairwise relationships and distributions of all numeric variables in the DataFrame.

Boxplots¶

A boxplot,also known as a box-and-whisker plot,is a graphical representation used to visualize the distribution and summary statistics of a dataset.

In [40]:
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
In [41]:
sns.boxplot(y='bedrooms',data=df, width=0.2)
Out[41]:
<Axes: ylabel='bedrooms'>
No description has been provided for this image

A vertical box plot of bedrooms with a box width of 0.2, showing the distribution and summary statistics of the data.

In [42]:
df.hist(bins=50, figsize=(15, 15))
Out[42]:
array([[<Axes: title={'center': 'date'}>,
        <Axes: title={'center': 'price'}>,
        <Axes: title={'center': 'bedrooms'}>,
        <Axes: title={'center': 'bathrooms'}>],
       [<Axes: title={'center': 'sqft_living'}>,
        <Axes: title={'center': 'sqft_lot'}>,
        <Axes: title={'center': 'floors'}>,
        <Axes: title={'center': 'waterfront'}>],
       [<Axes: title={'center': 'view'}>,
        <Axes: title={'center': 'condition'}>,
        <Axes: title={'center': 'sqft_above'}>,
        <Axes: title={'center': 'sqft_basement'}>],
       [<Axes: title={'center': 'yr_built'}>,
        <Axes: title={'center': 'yr_renovated'}>, <Axes: >, <Axes: >]],
      dtype=object)
No description has been provided for this image

A grid of histograms for all numeric columns in the DataFrame, with 50 bins each, displayed in a 15x15 inch figure.

In [43]:
sns.boxplot(x='bedrooms',y='price',data=df)
Out[43]:
<Axes: xlabel='bedrooms', ylabel='price'>
No description has been provided for this image
In [44]:
plt.figure(figsize=(12,5))
sns.boxplot(data=df)
Out[44]:
<Axes: >
No description has been provided for this image
In [45]:
plt.figure(figsize=(10, 6))
for col in df.columns:
    sns.boxplot(df[col])
    plt.title(f"Distribution of {col}")
    plt.xlabel(col)
    plt.xticks(rotation=45)
    plt.show()
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Individual box plots for each column in the DataFrame, with titles, labeled x-axes, and rotated x-axis labels, displayed one at a time in a 10x6 inch figure.

Conclusion of House Price Prediction Data Analysis¶

The house price prediction dataset enables the development of models to estimate property values based on various features and historical data.